Download Thinking Interactively with Visualizations

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Intro
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Graphics
Dimension
Interaction
Wrap-up
Data Exploration, Analysis, and Representation:
Integration through Visual Analytics
Remco Chang
UNC Charlotte
Charlotte Visualization Center
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Problem Statement
• The growth of data is
exceeding our ability to
analyze them.
• The amount of digital
information generated in
the years 2002, 2006,
2010:
– 2002: 22 EB (exabytes, 1018)
– 2006: 161 EB
– 2010: 988 EB (almost 1 ZB)
1: Data courtesy of Dr. Joseph Kielman, DHS
2: Image courtesy of Dr. Maria Zemankova, NSF
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Problem Statement
• The data is often complex,
ambiguous, noisy.
Analysis of which requires
human understanding.
– About 2 GB of digital
information is being
produced per person per
year
– 95% of the Digital
Universe’s information is
unstructured
1: Data courtesy of Dr. Joseph Kielman, DHS
2: Image courtesy of Dr. Maria Zemankova, NSF
Intro
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Example: What Does Fraud Look Like?
• Financial Institutions like Bank of America have legal responsibilities to
report all suspicious activities
• Data size: approximately 200,000 transactions per day (73 million
transactions per year)
• Problems:
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Automated approach can only detect known patterns
Bad guys are smart: patterns are constantly changing
No single transaction appears fraudulent
Few experts: fraud detection is considered an “art”
Data is messy: lack of international standards resulting in ambiguous data
• Current methods:
– 10 analysts monitoring and analyzing all transactions
– Using SQL queries and spreadsheet-like interfaces
– Limited to the time scale (2 weeks)
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Eureka: Visual Analytics!!
“Saunders, perhaps you’re getting a bit carried away
with the visual analytics!”1
1: Slide courtesy of Dr. Maria Zemankova, NSF
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WireVis: Financial Fraud Analysis
• In collaboration with Bank of America
– Looks for suspicious wire transactions
– Currently beta-deployed at WireWatch
– Visualizes 7 million transactions over 1 year
• Uses interaction to coordinate four perspectives:
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Keywords to Accounts
Keywords to Keywords
Keywords/Accounts over Time
Account similarities (search by example)
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WireVis: Financial Fraud Analysis
Heatmap View
(Accounts to Keywords
Relationship)
Search by Example
(Find Similar
Accounts)
Keyword Network
(Keyword
Relationships)
Strings and Beads
(Relationships over Time)
R. Chang et al., Scalable and interactive visual analysis of financial wire transactions for fraud detection. Information Visualization,2008.
R. Chang et al., Wirevis: Visualization of categorical, time-varying data from financial transactions. IEEE VAST, 2007.
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What is Visual Analytics?
• Visual analytics is the science of analytical reasoning
facilitated by interactive visual interfaces [Thomas &
Cook 2005]
• Since 2004, the field has grown
significantly. Aside from tens to
hundreds of domestic and
international partners, it now
has a IEEE conference (IEEE
VAST), an NSF program
(FODAVA), and a forthcoming
IEEE Transactions journal.
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Individually Not Unique
• Data Mining
• Machine
Learning
• Databases
• Information
Retrieval
• etc
Analytical
Reasoning
and
Interaction
Data
Representation
Transformation
Production,
Presentation
Dissemination
• Tech Transfer
• Report Generation
• etc
•
•
•
•
Interaction Design
Cognitive Psychology
Intelligence Analysis
etc.
Visual
Representation
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•
•
•
InfoVis
SciVis
Graphics
etc
Validation
and
Evaluation
• Quality Assurance
• User studies (HCI)
• etc
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In Combinations of 2 or 3…
• Data Mining
• Machine
Learning
• Databases
• Information
Retrieval
• etc
Analytical
Reasoning
and
Interaction
Data
Representation
Transformation
Production,
Presentation
Dissemination
Visual
Representation
Validation
and
Evaluation
•
•
•
•
InfoVis
SciVis
Graphics
etc
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In Combinations of 2 or 3…
Analytical
Reasoning
and
Interaction
Data
Representation
Transformation
Production,
Presentation
Dissemination
• Tech Transfer
• Report Generation
• etc
•
•
•
•
Interaction Design
Cognitive Psychology
Intelligence Analysis
etc.
Visual
Representation
Validation
and
Evaluation
Wrap-up
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Case Study on WireVis
• User Centric
– Designed system based on
domain expertise
• Visual Interface
– Multiple coordinated views
that link multiple dimensions
• Interactive
– Overview, drill-down,
reclustering
• Data Clustering
Analytical
Reasoning
and
Interaction
Data
Representat
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Transformat
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Production,
Presentatio
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Disseminati
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Visual
Represent
ation
– Clustering by accounts, and
search by example
• Production
– Connected to a live database
and beta-deployed at BofA
Validation
and
Evaluation
• (Validation)
– Expert evaluation
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This Talk Focuses On…
• Data Mining
• Machine
Learning
• Databases
• Information
Retrieval
• etc
Analytical
Reasoning
and
Interaction
Data
Representation
Transformation
Production,
Presentation
Dissemination
•
•
•
•
Interaction Design
Cognitive Psychology
Intelligence Analysis
etc.
Visual
Representation
Validation
and
Evaluation
•
•
•
•
InfoVis
SciVis
Graphics
etc
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Visual Analytics, A Graphics Perspective
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Visual Analytics, A Graphics Perspective
• Master’s Thesis -– Simulating dynamic motion
based on kinematic motion
• Jiggling of muscles
– Skinnable Mesh
• Volumetric deformation
– Compared 3 types of massspring systems
• Regular (unconstrained) massspring
• Reduced degree of freedom
• Approximate finite element
method with implicit
integration
• Is this applicable beyond
graphics and simulation?
R. Chang, Simulation Techniques for Deformable Animated Characters. Master’s Thesis, Brown University, 2000.
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From Graphics to Visual Analytics:
An Example in Urban Simplification
• (left) Original model, 285k polygons
• (center) e=100, 129k polygons (45% of original)
• (right) e=1000, 53k polygons (18% of original)
R. Chang et al., Legible simplification of textured urban models. IEEE Computer Graphics and Applications, 28(3):27–36, 2008.
R. Chang et al., Hierarchical simplification of city models to maintain urban legibility. ACM SIGGRAPH 2006 Sketches, page 130 , 2006.
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Urban Simplification
• Which polygons to remove?
Original Model
Our Textured Model
Simplified Model
using QSlim
Our Model
Visually different, but quantitatively similar!
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Urban Simplification
• The goal is to retain the “Image of the City”
• Based on Kevin Lynch’s concept of “Urban
Legibility” [1960]
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Paths: highways, railroads
Edges: shorelines, boundaries
Districts: industrial, historic
Nodes: Time Square in NYC
Landmarks: Empire State building
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Algorithm to Preserve Legibility
• Identify and preserve Paths and Edges
• Create logical Districts and Nodes
• Simplify model while preserving Paths, Edges, Districts, and
Nodes
• Hierarchically apply appropriate amount of texture
• Highlight Landmarks and choose models to render
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Algorithm for Preserving Legibility
• Paths & Edges
– Hierarchical (singlelink) clustering
• Nodes
– Merging clusters
– Polyline
simplification using
convex hulls
• Landmarks
– Pixel-based skyline
preservation
• That’s pretty good,
right?
Wrap-up
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Urban Visualization with Semantics
• How do people think about a city?
– Describe New York…
• Response 1: “New York is large, compact, and crowded.”
• Response 2: “The area where I live there has a strong mix of
ethnicities.”
Geometric, Information, View Dependent (Cognitive)
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Urban Visualization
• Geometric
– Create a hierarchy of shapes based on the rules of legibility
• Information
– Matrix view and Parallel Coordinates show relationships between clusters and
dimensions
• View Dependence (Cognitive)
– Uses interaction to alter the position of focus
R. Chang et al., Legible cities: Focus-dependent multi-resolution visualization of urban relationships. IEEE Transactions on Visualization
and Graphics , 13(6):1169–1175, 2007
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Probe-based Interactions
• Using Probes allows for comparing multiple
regions-of-interest simultaneously
R. Chang et al., Multi-focused geospatial analysis using probes. Visualization and Computer Graphics, IEEE Transactions on, 14(6):1165–
1172, Nov.-Dec. 2008.
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Urban Visualization
Graphics + Visual Analytics
• Applying graphics approaches
– Data transformation
– Screen-based metrics
– Hardware acceleration
• Applying visual analytics
principles
– Multi-dimensional data
representation
– Interactive exploration
– Broader applicability
Interaction
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Extending Visual Analytics Principles
Who
• Global Terrorism
Database
– Application of the
investigative 5 W’s
• Bridge Maintenance
Where
What
Evidence
Box
Original
Data
– Exploring subjective
inspection reports
• Biomechanical
Motion
– Interactive motion
comparison
methods
R. Chang et al., Investigative Visual Analysis of Global Terrorism, Journal of Computer Graphics Forum, 2008.
When
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Extending Visual Analytics Principles
• Global Terrorism
Database
– Application of the
investigative 5 W’s
• Bridge Maintenance
– Exploring subjective
inspection reports
• Biomechanical
Motion
– Interactive motion
comparison
methods
R. Chang et al., An Interactive Visual Analytics System for Bridge Management, Journal of Computer Graphics Forum, 2010. To Appear.
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Extending Visual Analytics Principles
• Global Terrorism
Database
– Application of the
investigative 5 W’s
• Bridge Maintenance
– Exploring subjective
inspection reports
• Biomechanical
Motion
– Interactive motion
comparison
methods
R. Chang et al., Interactive Coordinated Multiple-View Visualization of Biomechanical Motion Data, IEEE Vis (TVCG) 2009.
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Human + Computer
A Mixed-Initiative Perspective
• So far, our approach is mostly user-driven
• Human vs. Artificial Intelligence
Garry Kasparov vs. Deep Blue (1997)
– Computer takes a “brute force” approach without analysis
– “As for how many moves ahead a grandmaster sees,” Kasparov concludes: “Just
one, the best one”
• Artificial Intelligence vs. Augmented Intelligence
Hydra vs. Cyborgs (1998)
– Grandmaster + 1 computer > Hydra (equiv. of Deep Blue)
– Amateur + 3 computers > Grandmaster + 1 computer1
• How to systematically repeat the success?
– Unsupervised machine learning + User
– User’s interactions with the computer
1. http://www.collisiondetection.net/mt/archives/2010/02/why_cyborgs_are.php
Computer
Translation
Human
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Human + Computer:
Dimension Reduction – Lost in Translation
• Biomechanical motion analysis revisited…
– 6 degrees of freedom (x, y, z rotation and x, y, z translation)
– One single joint
• Applying a non-linear
dimension reduction method
(manifold learning)
– Isomap
– MDS embedding
• We found:
– 3 latent dimensions
– 2 of which are ambiguous…
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Human + Computer:
Dimension Reduction
• Non-linear is too hard. Let’s start with a classical linear approach,
principle component analysis (PCA).
• Quick Refresher of PCA
– Find most dominant eigenvectors as principle components
– Data points are re-projected into the new coordinate system
• For many (especially novices), PCA is easy to understand
mathematically, but difficult to understand “semantically”.
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Human + Computer:
Exploring Dimension Reduction: iPCA
R. Chang et al., iPCA: An Interactive System for PCA-based Visual Analytics. Computer Graphics Forum (Eurovis), 2009.
Wrap-up
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Human + Computer:
Comparing iPCA to SAS/INSIGHT
• Results
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A bit more accurate
Not faster
People don’t “give up”
Users seem to understand
the intuition behind PCA
better
• Overall preference
– Using letter grades (A through
F) with “A” representing
excellent and F a failing grade.
• A lot more work needs to be
done…
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Human + Computer:
User Interactions
• We can use interactions to… [Yi et al. 2007]
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Select: mark something as interesting
Explore: show me something else
Reconfigure: show me a different arrangement
Encode: show me a different representation
Abstract/Elaborate: show me more or less detail
Filter: show me something conditionally
Connect: show me related items
• In other words, we can use interactions to think.
Wrap-up
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If (Interactions == Thinking)…
• What is in a user’s interactions?
• If (interactions == thinking), what can we learn
from the user’s interactions?
• Is it possible to extract “thinking” from
“interactions”?
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What is in a User’s Interactions?
• Goal: determine if there really is “thinking” in a
user’s interactions.
Grad
Students
(Coders)
Compare!
(manually)
Analysts
Strategies
Methods
Findings
Guesses of
Analysts’
thinking
Logged
(semantic)
Interactions
WireVis
Interaction-Log Vis
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What’s in a User’s Interactions
• From this experiment, we find that interactions contains at least:
– 60% of the (high level) strategies
– 60% of the (mid level) methods
– 79% of the (low level) findings
R. Chang et al., Recovering Reasoning Process From User Interactions. IEEE Computer Graphics and Applications, 2009.
R. Chang et al., Evaluating the Relationship Between User Interaction and Financial Visual Analysis. IEEE Symposium on VAST, 2009.
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What’s in a User’s Interactions
• Why are these so much
lower than others?
– (recovering “methods” at
about 15%)
• Only capturing a user’s
interaction in this case is
insufficient.
Intro
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User Interactions, An Analytical Approach
• Now that we’ve shown that (interaction ~= thinking)
– Can we automate the process?
• Formulate every user interaction as a fixed-length vector (Design
Galaries [Marks et al. Siggraph 97]). For example,
•
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User interaction in the left application can be represented as a single dimensional
vector <P>
User interaction in the right application can be represented as a two dimensional
vector <P, S>
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Human + Computer:
User Interactions – Lessons Learned
• We have proven that a great deal of an analyst’s “thinking” in using a
visualization is capturable and extractable.
– Although the study is limited in scope, it establishes a foundation for
interaction-capturing related research
• With interaction capturing, we might be able to collect all the thinking of
expert analysts and create a knowledge database that is useful for
– Training: many domain specific analytics tasks are difficult to teach
– Guidance: use existing knowledge to guide future analyses
– Verification, and validation: check to see if everything was done right.
• But not all visualizations are interactive, and not all thinking is reflected in
the interactions.
– A model of how and what to capture in a visualization process is necessary.
• Automating the process of extracting thinking is the key.
– By formulating user interactions as high dimensional vectors, we can apply
analytical methods
– The assumption is that the vectors are fixed-length. Is this always true?
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Conclusion
• Visual Analytics is a growing new
area that is looking to address
some pressing needs
Analytical
Reasoning
and
Interaction
Data
Representat
ion
Transformat
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Production,
Presentatio
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Disseminati
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Visual
Represent
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Validation
and
Evaluation
– Too much (messy) data, too little
time
• By combining strengths and
findings in existing disciplines, we
have demonstrated that
– There are some great benefits
– But there are also some difficult
challenges
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Summary of Contribution
• Contributions
– Visual Representation
• Urban modeling and visualization
– Interaction + Visual Representation
• Role of interactivity in visual thinking
• Applying principles to real-world
problems such as financial analytics,
terrorism studies, bridge management,
biomechanical motion analysis, etc.
– Interaction + Data Analysis
• Exploring principle component analysis
– Evaluation + Interaction
• Proposed a new learning-based
evaluation methods
• In particular, my background in
computer graphics helps the
development of a human + computer
research agenda
Interaction
Wrap-up
Intro
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Future Work (Funded Projects)
•
NSF SciSIP:
– Title: A Visual Analytics Approach to Science and Innovation Policy.
•
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PI: William Ribarsky, Co-PIs: Jim Thomas, Remco Chang, Jing Yang.
$746,567. 2009-2012 (3 years).
– Abstract: developing metrics and visual tools for identifying patterns in science policies.
•
NSF/DOD (Minerva Initiative):
– Title: Collaborative Project: Terror, Conflict Processes, Organizations, & Ideologies: Completing
the Picture.
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PI: Remco Chang
$100,000. 2009-2010 (2 years).
– Abstract: design and develop visual analytical tools to identifying the causal relationships in
government policies and domestic conflicts.
•
DHS International Program:
– Title: Deriving and Applying Cognitive Principles for Human/Computer Approaches to
Complex Analytical Problems.
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PI: William Ribarsky, Co-PIs: Brian Fisher, Remco Chang, John Dill.
$200,000. 2009-2010 (1 year).
– Abstract: identifying new evaluation methods for visual analytical systems, and applying
computational methods for analyzing user interactions.
•
Quantitative Analysis Division at Bank of America (Dr. Agus Sudjianto)
– Exploration and analysis of financial risk
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Future Work (On-going Collaborations)
• With NSF FODAVA Center at Georgia Tech (Dr. Haesun Park, director)
– Interpreting user interactions to affecting machine learning algorithms
– Visual PCA: using perceptual metrics to finding principle components
– Applying perceptual constraint to dimension reduction: for animating
temporal data in dimension reduction, find methods to maintain hysteresis
• With University of Kentucky (Drs. Judy Goldsmith, Jinze Liu, Phillip Chang,
MD)
– Integrating data mining (KDD), POMDP, and visual analytics to preventing
sepsis by identifying biomarkers (Proposal in submission to NSF CDI)
• With geographer and architect at UNC Charlotte (Dr. Jean-Claude Thill and
Eric Sauda)
– Designing computational methods for identifying neighborhood characteristics
(Proposal in submission to NSF IIS)
– Applying the UrbanVis system to analyzing crime (proposal in preparation for
DOJ/NIJ)
• With Virginia Tech (Dr. Chris North)
– Developing a research agenda for analytic provenance
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Journal Publications (16)
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Urban Visualization
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Visualization and Visual Analytics
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X. Wang, W. Dou, S.E. Chen, W. Ribarsky, and R. Chang. An interactive visual analytics system for bridge management. Computer
Graphics Forum (Eurovis 2010), 2010. Conditional acceptance.
D. Keefe, M. Ewert, W. Ribarsky, and R. Chang. Interactive coordinated multiple-view visualization of biomechanical motion data.
Visualization and Computer Graphics, IEEE Transactions on (IEEE Visualization Conference), 15(6):1383–1390, 2009
X. Wang, D.H. Jeong, W. Dou, S.W. Lee, W. Ribarsky, and R. Chang. Defining and applying knowledge conversion processes to a visual
analytics system. Computers & Graphics, July 2009. [Online] doi:10.1016/j.cag.2009.06.004
D.H. Jeong, C. Ziemkiewicz, B. Fisher, W. Ribarsky, and R. Chang. iPCA: An interactive system for PCA-based visual analytics. Computer
Graphics Forum, 28(3):767–774, 2009.
R. Chang, C. Ziemkiewicz, T.M. Green, and W. Ribarsky. Defining insight for visual analytics. IEEE Computer Graphics and Applications,
29(2):14–17, 2009.
R. Chang, A. Lee, M. Ghoniem, R. Kosara, W. Ribarsky, J. Yang, E. Suma, C. Ziemkiewicz, D. Kern, and A. Sudjianto. Scalable and interactive
visual analysis of financial wire transactions for fraud detection. Information Visualization, 7:63–76(14), 2008.
X. Wang, E. Miller, K. Smarick, W. Ribarsky, and R. Chang. Investigative visual analysis of global terrorism database. Computer Graphics
Forum, 27(3):919–926, 2008.
Interaction & Provenance
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•
R. Chang, T. Butkiewicz, C. Ziemkiewicz, Z. Wartell, N. Pollard, and W. Ribarsky. Legible simplification of textured urban models. IEEE
Computer Graphics and Applications, 28(3):27–36, 2008.
T. Butkiewicz, R. Chang, Z. Wartell, and W. Ribarsky. Visual analysis of urban change. Computer Graphics Forum, 27(3):903–910, 2008.
T. Butkiewicz, R. Chang, W. Ribarsky, and Z. Wartell. Understanding Dynamics of Geographic Domains, chapter Visual Analysis of Urban
Terrain Dynamics, pages 151– 169. CRC Press/Taylor and Francis, 2007.
R. Chang, G. Wessel, R. Kosara, E. Sauda, and W. Ribarsky. Legible cities: Focus-dependent multi-resolution visualization of urban
relationships. Visualization and Computer Graphics, IEEE Transactions on, 13(6):1169–1175, Nov.-Dec. 2007.
W. Pike, J. Stasko, R. Chang, and T. O’Connell. Science of interaction. Information Visualization, 8:263–274, 2009.
W. Dou, D.H. Jeong, F. Stukes, W. Ribarsky, H. Lipford, and R. Chang. Recovering reasoning process from user interactions. IEEE Computer
Graphics and Applications, 29(3):52–61, 2009
VR & Interface Designs
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T. Butkiewicz, R. Chang, Z. Wartell, and W. Ribarsky. Alleviating the modifiable areal unit problem with probe-based geospatial analyses.
Computer Graphics Forum (Eurovis 2010), 2010. Conditional acceptance
T. Butkiewicz, W. Dou, Z. Wartell, W. Ribarsky, and R. Chang. Multi-focused geospatial analysis using probes. Visualization and Computer
Graphics, IEEE Transactions on, 14(6):1165–1172, Nov.-Dec. 2008.
D.H. Jeong, C. Song, R. Chang, and L. Hodges. User experimentation: An evaluation of velocity control techniques in immersive virtual
environments. Springer-Verlag Virtual Reality, 13(1):41–50, Mar. 2009.
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Conference/Workshop (22)
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R. Chang, C. Ziemkiewicz, R. Pyzh, J. Kielman, and W. Ribarsky. Learning-based evaluation of visual analytics systems. In ACM SIGCHI BELIV Workshop, 2010.
Conditional acceptance.
D. H. Jeong, T. Green, W. Ribarsky, and R. Chang. Comparative evaluation of two interface tools in performing visual analytics tasks. In ACM SIGCHI BELIV
Workshop, 2010. Conditional acceptance.
G. Wessel, E. Unruh, R. Chang, and E. Sauda. Urban user interface: Urban legibility reconsidered. In Southwest ACSA, 2010.
D. H. Jeong, W. Dou, W. Ribarsky, and R. Chang. Knowledge-oriented refactoring in visualization. In IEEE Visualization Workshop on Refactoring Visualization
From Experience, 2009.
D. H. Jeong, W. Ribarsky, and R. Chang. Designing a PCA-based collaborative visual analytics system. In IEEE Visualization Workshop on Collaborative
Visualization, 2009.
W. Dou, D. H. Jeong, F. Stukes, W. Ribarsky, H. Lipford, and R. Chang. Comparing usage patterns of domain experts and novices in visual analytical tasks. In ACM
SIGCHI Sensemaking Workshop 2009.
X. Wang, W. Dou, R. Vatcha, W. Liu, S. E. Chen, S. W. Lee, R. Chang, and W. Ribarsky. Knowledge integrated visual analysis of bridge safety and maintenance. In
SPIE 2009.
X. Wang, W. Dou, W. Ribarsky, and R. Chang. Integration of heterogeneous processes through visual analytics. In SPIE 2009,.
M. Butkiewicz, T. Butkiewicz, W. Ribarsky, and R. Chang. Integrating timeseries visualizations within parallel coordinates for exploratory analysis of incident
databases. SPIE 2009.
T. Butkiewicz, D. H. Jeong, W. Ribarsky, and R. Chang. Hierarchical multitouch selection techniques for collaborative geospatial analysis. In SPIE Defense, Security
and Sensing 2009.
D. H. Jeong, R. Chang, and W. Ribarsky. An alternative definition and model for knowledge visualization. In IEEE Visualization Workshop on Knowledge Assisted
Visualization, 2008.
X. Wang, W. Dou, S. W. Lee, W. Ribarsky, and R. Chang. Integrating visual analysis with ontological knowledge structure. In IEEE Workshop on Knowledge
Assisted Visualization, 2008.
D. H. Jeong, W. Dou, F. Stukes, W. Ribarsky, H. Lipford, and R. Chang. Evaluating the relationship between user interaction and financial visual analysis. In Visual
Analytics Science and Technology. IEEE Symposium on, 2008.
G. Wessel, R. Chang, and E. Sauda. Towards a new (mapping of the) city: Interactive, data rich modes of urban legibility. In Association for Computer Aided
Design in Architecture, 2008.
G. Wessel, R. Chang, and E. Sauda. Visualizing GIS: Urban form and data structure. Seeking the City: Visionaries on the Margins, ACSA, 2008.
G. Wessel, E. Sauda, and R. Chang. Urban visualization: Urban design and computer visualization. In CAADRIA 2008.
T. Butkiewicz, R. Chang, Z. Wartell, and W. Ribarsky. Visual analysis for live lidar battlefield change detection. SPIE, 2008.
J. Jones, R. Chang, T. Butkiewicz, and W. Ribarsky. Visualizing uncertainty for geographical information in the global terrorism database. SPIE, 2008.
A. Godwin, R. Chang, R. Kosara, and W. Ribarsky. Visual analysis of entity relationships in the global terrorism database. SPIE, 2008.
T. Butkiewicz, R. Chang, Z. Wartell, and W. Ribarsky. Analyzing sampled terrain volumetrically with regard to error and geologic variation. SPIE, 2007.
R. Chang, M. Ghoniem, R. Kosara, W. Ribarsky, J. Yang, E. Suma, C. Ziemkiewicz, D. Kern, and A. Sudjianto. Wirevis: Visualization of categorical, time-varying
data from financial transactions. In Visual Analytics Science and Technology, 2007, IEEE Symposium on, 2007.
R. Chang, T. Butkiewicz, C. Ziemkiewicz, Z. Wartell, N. Pollard, and W. Ribarsky. Hierarchical simplification of city models to maintain urban legibility. In
SIGGRAPH ’06: ACM SIGGRAPH 2006 Sketches, 2006.
R. Chang, R. Kosara, A. Godwin, and W. Ribarsky. Towards a role of visualization in social modeling. AAAI 2009 Spring Symposium on Technosocial Predictive
Analytics, 2009.
G. Wessel, E. Sauda, and R. Chang. Mapping understanding:Transforming topographic maps into cognitive maps. GeoVis Hamburg Workshop, 2009.
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Acknowledgement
From the Data Visualization Group (DVG) at UNC Charlotte
Bill Ribarsky
Zach Wartell
Dong Hyun Jeong, Tom Butkiewicz, Xiaoyu Wang, Wenwen Dou, Tera Green
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Acknowledgement
From the Urban Visualization Group at UNC Charlotte
Eric Sauda
Jean-Claude Thill
Ginette Wessel
Elizabeth Unruh
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Acknowledgement
More Collaborators…
Clockwise, starting on the left:
Nancy Pollard, Evan Suma, Heather Lipford, Dan Keefe, Caroline Ziemkiewicz, Robert Kosara, Mohammad Ghoniem
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Acknowledgement
• And many many others…
Joseph Kielman, Bill Pike, Theresa O'Connell, SeokWon Lee, Brian Fisher, Alvin Lee, Jing Yang, Daniel
Kern, Agust Sudjianto, Erin Miller, Kathleen
Smarick, Felesia Stukes, Marcus Ewert, Larry
Hodges, Michael Butkiewicz, Josh Jones, Alex
Godwin, Edd Hauser, Shenen Chen, Bill Tolone,
Wanqiu Liu, Rashna Vatcha
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Thank you!
[email protected]
http://www.viscenter.uncc.edu/~rchang
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Backup Slides – Professional Activities
Wrap-up
Intro
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Interaction
Wrap-up
Grants Awarded (3)
•
NSF SciSIP:
– A Visual Analytics Approach to Science and Innovation Policy.
•
•
•
PI: William Ribarsky, Co-PIs: Jim Thomas, Remco Chang, Jing Yang.
$746,567. 2009-2012 (3 years).
NSF BCS:
– Collaborative Project: Terror, Conflict Processes, Organizations, & Ideologies: Completing the
Picture.
•
•
•
PI: Remco Chang
$100,000. 2009-2010 (2 years).
DHS International Program:
– Deriving and Applying Cognitive Principles for Human/Computer Approaches to Complex
Analytical Problems.
•
•
•
PI: William Ribarsky, Co-PIs: Brian Fisher, Remco Chang, John Dill.
$200,000. 2009-2010 (1 year).
In Submission / Preparation
– 2 other NSF proposals are pending reviews
– 1 DOH/NIJ preparation
Intro
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Professional Activities
•
Committee / Panelists
–
–
–
–
–
•
Program Committee: IEEE Conference on Visual Analytics, 2010
Program Committee: SIG CHI Workshop on BELIV, 2010
Program Committee: AAAI Spring-09 Symposium on Technosocial Predictive Analytics, 2009
Panelist: 3rd Annual DHS University Summit. Panel: Research to Reality, 2009
Panelist: 3rd Annual DHS University Summit. Panel: Visual Analytics and Discrete Science Integration into the DHS Center of
Excellence Program, 2009
Invited Talks
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
Dec 13, 2006 Google Inc. Simplification of Urban Models based on Urban Legibility
July 6, 2007 Naval Research Lab. Urban Visualization
Oct 4, 2007 Charlotte Viscenter. Urban Visualization
Oct 17, 2007 Charlotte Metropolitan GIS Users Group. GIS and Urban Visualization
Nov 19, 2007 START Center at University of Maryland. Integrated Visual Analysis of the Global Terrorism Database
Nov 29, 2007 Charlotte Viscenter. Integrated Visual Analysis of the Global Terrorism Database
Jan 25, 2008 DoD/DHS Social Science Modeling and Information Visualization Symposium. Social Science and Information
Visualization on Terrorism and Multimedia
May 14, 2008 Charlotte Metropolitan GIS User Group. Multi-Focused Geospatial Analysis Using Probes
Aug 27, 2008 DoD/DHS Symposium for Overcoming the Information Challenge in Federated Analysis: From Concept to Practice.
Roadmap of Visualization
Mar 19, 2009 DHS University Summit. Panel: Research to Reality
Mar 19, 2009 DHS University Summit. Panel: Visual Analytics and Discrete Science Integration into the DHS Center of Excellence
Program
Apr 27, 2009 University of Kentucky. Thinking Interactively with Visualization
May 29, 2009 University of Victoria. Thinking Interactively with Visualization
Jul 28, 2009 Pacific Northwest National Lab. Thinking Interactively with Visualization
Jul 30, 2009 Microsoft Research. Thinking Interactively with Visualization
Aug 19, 2009 National Visual Analytic Consortium. What Are Your Interactions Doing For Your Visualization?
Sep 30, 2009 University of Kentucky (Grand Rounds at the Department of Surgery). Preventing
Sepsis: Artificial Intelligence, Knowledge Discovery, and Visualization
Jan 21, 2010 Charlotte Viscenter. UrbanVis Research Group: Urban Analytics
Feb 25, 2010 University of Georgia (AI Institute). Thinking Interactively with Visualization
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Backup Slides – Urban Simplification
Wrap-up
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Identifying and Preserving
Edges
Interaction
Wrap-up
Paths and
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Identifying and Preserving
Paths and Edges (1)
a
b
c
d
bc
• Single-Link Clustering
– Iteratively groups the “closest” clusters
together based on Euclidean distance
– produces a binary tree (dendrogram)
– Penalizes large clusters to create a more
balanced tree
e
de
def
abc
bcdef
abcdef
f
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Identifying and Preserving
Paths and Edges (2)
Interaction
Wrap-up
58/50
Intro
VA
Creating logical
and Nodes
Graphics
Dimension
Interaction
Wrap-up
Districts
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VA
Creating logical
Nodes (1)
Graphics
Dimension
Interaction
Wrap-up
Districts and
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Creating logical
Nodes (2)
Interaction
Wrap-up
Districts and
• Merge two clusters by combining footprints
(a)
(b)
(c)
• (c) The resulting “Merged Hull”
• (d) The Introduced Error, or “Negative Space”
(d)
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Simplification while preserving
Edges, Nodes, and Districts
Interaction
Paths,
Wrap-up
62/50
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Simplification while preserving
Edges, Nodes, and Districts (1)
6000 edges
Interaction
Wrap-up
Paths,
1000 edges
Demo!
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Simplification while preserving
Edges, Nodes, and Districts (2)
Interaction
Wrap-up
Paths,
• After the polylines have been simplified
– Create “Cluster Meshes”
– The height of the Cluster Mesh is the median height of all buildings
in the cluster
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Hierarchical Textures
Dimension
Interaction
Wrap-up
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Hierarchical Textures (1)
• Each Cluster Mesh contains 6 textures
– 1 Side Texture
– 1 top-down view of the roof texture
– 4 roof textures from 4 angles
(south, west, east, north)
Top-down
South
Side texture
West
East
North
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Hierarchical Textures (2)
• Clusters are divided into “bins” based on their visual importance
• Each bin contains a texture atlas
• Texture atlases from all bins have the same dimension
n/2
n/4
n/8
….
…
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Runtime Levels of Detail
Interaction
Wrap-up
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Runtime Levels of Detail
• Starting with the root node of
abcdef
the dendrogram
– Approximate the “Negative
abc
Space” as a 3D box – shown as
the red box
def
– Project the visible sides of the
bc
box onto screen space
– Reject if the number of pixel is
above a user-defined tolerance
a
b
de
c
d
e
f
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Wrap-up
Landmark and Skyline Preservation (1)
Original Skyline
Without Landmark
Preservation
With Landmark
Preservation
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Wrap-up
Landmark and Skyline Preservation (2)
– Project a user-defined pixel tolerance (α) onto the top of each cluster
– If any building within that cluster is taller than the projected
tolerance (shown in green), it is drawn separately from the cluster
mesh.
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Intro
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(2) Investigative GTD
Who
Where
What
Evidence
Box
Original
Data
R. Chang et al., Investigative Visual Analysis of Global Terrorism, Journal of Computer Graphics Forum (Eurovis), 2008.
When
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(2) Investigative GTD:
Revealing Global Strategy
This group’s attacks
are not bounded by
geo-locations but
instead, religious
beliefs.
Its attack patterns
changed with its
developments.
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(2) Investigative GTD:
Discovering Unexpected Temporal Pattern
A geographicallybounded entity in the
Philippines.
The ThemeRiver shows
its rise and fall as an
entity and its modus
operandi.
Domestic Group
Wrap-up
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What is in a User’s Interactions?
Keyboard, Mouse, etc
Input
Visualization
Human
Output
Images (monitor)
• Types of Human-Visualization Interactions
– Word editing (input heavy, little output)
– Browsing, watching a movie (output heavy, little input)
– Visual Analysis (closer to 50-50)
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Discussion
• What interactivity is not good for:
– Presentation
– YMMV = “your mileage may vary”
• Reproducibility: Users behave differently each time.
• Evaluation is difficult due to opportunistic discoveries..
– Often sacrifices accuracy
• iPCA – SVD takes time on large datasets, use iterative
approximation algorithms such as onlineSVD.
• WireVis – Clustering of large datasets is slow. Either
pre-compute or use more trivial “binning” methods.
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Discussion
• Interestingly,
– It doesn’t save you time…
– And it doesn’t make a user more
accurate in performing a task.
• However, there are empirical
evidence that using interactivity:
– Users are more engaged (don’t
give up)
– Users prefer these systems over
static (query-based) systems
– Users have a faster learning curve
• We need better measurements
to determine the “benefits of
interactivity”
Interaction
Wrap-up